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 Bias (27)
 Monte Carlo simulation (25)
 Bootstrap (24)
 Simulation (24)
 Power (23)

 Confidence interval (22)
 Type I error (19)
 Mean squared error (19)
 Effect size (19)
 Sample size (18)
 Multicollinearity (17)
 Monte Carlo (17)
 Permutation test (17)
 Robustness (17)
 Missing data (15)
 SPSS (13)
 Efficiency (13)
 Reliability (13)
 Confidence intervals (13)
 Heteroscedasticity (13)
 Pvalue (12)
 Maximum likelihood estimation (12)
 Logistic regression (12)
 Nonnormality (11)
 Simple random sampling (11)
 Estimation (11)
 Mean square error (10)
 ANOVA (10)
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Articles 1  30 of 1028
FullText Articles in Social and Behavioral Sciences
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Journal of Modern Applied Statistical Methods
Longitudinal data analyses commonly assume that time intervals are predetermined and have no information regarding the outcomes. However, there might be irregular time intervals and informative time. Presented are joint models and asymptotic behaviors of the parameter estimates. Also, the models are applied for real data sets.
Comparison Of Scale Identification Methods In Mixture Irt Models, YounJeng Choi, Allan S. Cohen
Comparison Of Scale Identification Methods In Mixture Irt Models, YounJeng Choi, Allan S. Cohen
Journal of Modern Applied Statistical Methods
The effects of three scale identification constraints in mixture IRT models were studied. A simulation study found no constraint effect on the mixture Rasch and mixture 2PL models, but the item anchoring constraint was the only one that worked well on selecting correct model with the mixture 3PL model.
Comparing Means Under Heteroscedasticity And Nonnormality: Further Exploring Robust Means Modeling, Alyssa Counsell, Robert Philip Chalmers, Robert A. Cribbie
Comparing Means Under Heteroscedasticity And Nonnormality: Further Exploring Robust Means Modeling, Alyssa Counsell, Robert Philip Chalmers, Robert A. Cribbie
Journal of Modern Applied Statistical Methods
Comparing the means of independent groups is a concern when the assumptions of normality and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVAtype procedures when the assumptions of normality and variance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error ...
Inferences About The Probability Of Success, Given The Value Of A Covariate, Using A Nonparametric Smoother, Rand Wilcox
Inferences About The Probability Of Success, Given The Value Of A Covariate, Using A Nonparametric Smoother, Rand Wilcox
Journal of Modern Applied Statistical Methods
For a binary random variable Y, let p(x) = P(Y = 1  X = x) for some covariate X. The goal of computing a confidence interval for p(x) is considered. In the logistic regression model, even a slight departure difficult to detect via a goodnessoffit test can yield inaccurate results. The accuracy of a confidence interval can deteriorate as the sample size increases. The goal is to suggest an alternative approach based on a smoother, which provides a more flexible approximation of p(x).
A Note On Inferences About The Probability Of Success, Rand Wilcox
A Note On Inferences About The Probability Of Success, Rand Wilcox
Journal of Modern Applied Statistical Methods
There is an extensive literature dealing with inferences about the probability of success. A minor goal in this note is to point out when certain recommended methods can be unsatisfactory when the sample size is small. The main goal is to report results on the twosample case. Extant results suggest using one of four methods. The results indicate when computing a 0.95 confidence interval, two of these methods can be more satisfactory when dealing with small sample sizes.
On Statistical Significance Of Discriminant Function Coefficients, Tolulope T. Sajobi, Gordon H. Fick, Lisa M. Lix
On Statistical Significance Of Discriminant Function Coefficients, Tolulope T. Sajobi, Gordon H. Fick, Lisa M. Lix
Journal of Modern Applied Statistical Methods
Discriminant function coefficients are useful for describing group differences and identifying variables that distinguish between groups. Test procedures were compared based on asymptotically approximations, empirical, and exact distributions for testing hypotheses about discriminant function coefficients. These tests are useful for assessing variable importance in multivariate group designs.
Support Vector MachineBased Modified Sp Statistic For Subset Selection With NonNormal Error Terms, Shivaji Shripati Desai, D N. Kashid
Support Vector MachineBased Modified Sp Statistic For Subset Selection With NonNormal Error Terms, Shivaji Shripati Desai, D N. Kashid
Journal of Modern Applied Statistical Methods
Support vector machine (SVM) is used for estimation of regression parameters to modify the sum of cross products (Sp). It works well for some nonnormal error distributions. The performance of existing robust methods and the modified Sp is evaluated through simulated and real data. The results show the performance of the modified Sp is good.
An Improved Two IndependentSamples Randomization Test For SingleCase AbType Intervention Designs: A 20Year Journey, Joel R. Levin, John M. Ferron, Boris S. Gafurov
An Improved Two IndependentSamples Randomization Test For SingleCase AbType Intervention Designs: A 20Year Journey, Joel R. Levin, John M. Ferron, Boris S. Gafurov
Journal of Modern Applied Statistical Methods
Detailed is a 20year arduous journey to develop a statistically viable twophase (AB) singlecase two independentsamples randomization test procedure. The test is designed to compare the effectiveness of two different interventions that are randomly assigned to cases. In contrast to the unsatisfactory simulation results produced by an earlier proposed randomization test, the present test consistently exhibited acceptable Type I error control under various design and effecttype configurations, while at the same time possessing adequate power to detect moderately sized interventiondifference effects. Selected issues, applications, and a multiplebaseline extension of the twosample test are discussed.
Recurrence Relations For Marginal And Joint Moment Generating Functions Of ToppLeone Generated Exponential Distribution Based On Record Values And Its Characterization, Zaki Anwar, Neetu Gupta, Mohd Akram Raza Khan, Qazi Azhad Jamal
Recurrence Relations For Marginal And Joint Moment Generating Functions Of ToppLeone Generated Exponential Distribution Based On Record Values And Its Characterization, Zaki Anwar, Neetu Gupta, Mohd Akram Raza Khan, Qazi Azhad Jamal
Journal of Modern Applied Statistical Methods
The exact expressions and some recurrence relations are derived for marginal and joint moment generating functions of k^{th} lower record values from ToppLeone Generated (TLG) Exponential distribution. This distribution is characterized by using the recurrence relation of the marginal moment generating function of k^{th} lower record values.
A New Exponential Approach For Reducing The Mean Squared Errors Of The Estimators Of Population Mean Using Conventional And NonConventional Location Parameters, Housila P. Singh, Anita Yadav
A New Exponential Approach For Reducing The Mean Squared Errors Of The Estimators Of Population Mean Using Conventional And NonConventional Location Parameters, Housila P. Singh, Anita Yadav
Journal of Modern Applied Statistical Methods
Classes of ratiotype estimators t (say) and ratiotype exponential estimators t_{e} (say) of the population mean are proposed, and their biases and mean squared errors under large sample approximation are presented. It is the class of ratiotype exponential estimators t_{e} provides estimators more efficient than the ratiotype estimators.
Logistic Growth Modeling With Markov Chain Monte Carlo Estimation, Jaehwa Choi, Jinsong Chen, Jeffrey R. Harring
Logistic Growth Modeling With Markov Chain Monte Carlo Estimation, Jaehwa Choi, Jinsong Chen, Jeffrey R. Harring
Journal of Modern Applied Statistical Methods
A new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes.
A Simulation Study On Increasing Capture Periods In Bayesian Closed Population CaptureRecapture Models With Heterogeneity, Ross M. Gosky, Joel Sanqui
A Simulation Study On Increasing Capture Periods In Bayesian Closed Population CaptureRecapture Models With Heterogeneity, Ross M. Gosky, Joel Sanqui
Journal of Modern Applied Statistical Methods
CaptureRecapture models are useful in estimating unknown population sizes. A common modeling challenge for closed population models involves modeling unequal animal catchability in each capture period, referred to as animal heterogeneity. Inference about population size N is dependent on the assumed distribution of animal capture probabilities in the population, and that different models can fit a data set equally well but provide contradictory inferences about N. Three common Bayesian CaptureRecapture heterogeneity models are studied with simulated data to study the prevalence of contradictory inferences is in different population sizes with relatively low capture probabilities, specifically at different numbers of capture ...
An Exploration Of Link Functions Used In Ordinal Regression, Thomas J. Smith, David A. Walker, Cornelius M. Mckenna
An Exploration Of Link Functions Used In Ordinal Regression, Thomas J. Smith, David A. Walker, Cornelius M. Mckenna
Journal of Modern Applied Statistical Methods
The purpose of this study is to examine issues involved with choice of a link function in generalized linear models with ordinal outcomes, including distributional appropriateness, link specificity, and palindromic invariance are discussed and an exemplar analysis provided using the Pew Research Center 25th anniversary of the Web Omnibus Survey data. Simulated data are used to compare the relative palindromic invariance of four distinct indices of determination/discrimination, including a newly proposed index by Smith et al. (2017).
Investigating The Performance Of Propensity Score Approaches For Differential Item Functioning Analysis, Yan Liu, Chanmin Kim, Amrey D. Wu, Paul Gustafson, Edward Kroc, Bruno D. Zumbo
Investigating The Performance Of Propensity Score Approaches For Differential Item Functioning Analysis, Yan Liu, Chanmin Kim, Amrey D. Wu, Paul Gustafson, Edward Kroc, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
To evaluate the performance of propensity score approaches for differential item functioning analysis, this simulation study was conducted to assess bias, mean square error, Type I error, and power under different levels of effect size and a variety of model misspecification conditions, including different types and missing patterns of covariates.
QuasiLikelihood Ratio Tests For Homoscedasticity In Linear Regression, Lili Yu, Varadan Sevilimedu, Robert Vogel, Hani Samawi
QuasiLikelihood Ratio Tests For Homoscedasticity In Linear Regression, Lili Yu, Varadan Sevilimedu, Robert Vogel, Hani Samawi
Journal of Modern Applied Statistical Methods
Two quasilikelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.
A Glossary On Building Longitudinal, PopulationBased Data Linkages To Explore Children’S Developmental Trajectories, Jennifer E. V. Lloyd, Jacqui Boonstra, Lisa Chen, Barry Forer, Ruth Hershler, Constance Milbrath, Brenda T. Poon, Neda Razaz, Pippa Rowcliffe, Kimberly SchonertReichl
A Glossary On Building Longitudinal, PopulationBased Data Linkages To Explore Children’S Developmental Trajectories, Jennifer E. V. Lloyd, Jacqui Boonstra, Lisa Chen, Barry Forer, Ruth Hershler, Constance Milbrath, Brenda T. Poon, Neda Razaz, Pippa Rowcliffe, Kimberly SchonertReichl
Journal of Modern Applied Statistical Methods
Populationbased, personspecific, longitudinal child and youth health and developmental data linkages involve connecting combinations of speciallycollected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.
Robust Confidence Intervals For The Population Mean Alternatives To The StudentT Confidence Interval, Moustafa Omar Ahmed AbuShawiesh, Aamir Saghir
Robust Confidence Intervals For The Population Mean Alternatives To The StudentT Confidence Interval, Moustafa Omar Ahmed AbuShawiesh, Aamir Saghir
Journal of Modern Applied Statistical Methods
In this paper, three robust confidence intervals are proposed as alternatives to the Student‑t confidence interval. The performance of these intervals was compared through a simulation study shows that Q_{n}t confidence interval performs the best and it is as good as Student’s‑t confidence interval. Reallife data was used for illustration and performing a comparison that support the findings obtained from the simulation study.
Using Spss To Analyze Complex Survey Data: A Primer, Danjie Zou, Jennifer E. V. Lloyd, Jennifer L. Baumbusch
Using Spss To Analyze Complex Survey Data: A Primer, Danjie Zou, Jennifer E. V. Lloyd, Jennifer L. Baumbusch
Journal of Modern Applied Statistical Methods
An introduction to using SPSS to analyze complex survey data is given. Key features of complex survey design are described briefly, including stratification, clustering, multiple stages, and weights. Then, annotated SPSS syntax for complex survey data analysis is presented to demonstrate the stepbystep process using real complex samples data.
On The Authentic Notion, Relevance, And Solution Of The JeffreysLindley Paradox In The Zettabyte Era, Miodrag M. Lovric
On The Authentic Notion, Relevance, And Solution Of The JeffreysLindley Paradox In The Zettabyte Era, Miodrag M. Lovric
Journal of Modern Applied Statistical Methods
The JeffreysLindley paradox is the most quoted divergence between the frequentist and Bayesian approaches to statistical inference. It is embedded in the very foundations of statistics and divides frequentist and Bayesian inference in an irreconcilable way. This paradox is the Gordian Knot of statistical inference and Data Science in the Zettabyte Era. If statistical science is ready for revolution confronted by the challenges of massive data sets analysis, the first step is to finally solve this anomaly. For more than sixty years, the JeffreysLindley paradox has been under active discussion and debate. Many solutions have been proposed, none entirely satisfactory ...
Conflicts In Bayesian Statistics Between Inference Based On Credible Intervals And Bayes Factors, Miodrag M. Lovric
Conflicts In Bayesian Statistics Between Inference Based On Credible Intervals And Bayes Factors, Miodrag M. Lovric
Journal of Modern Applied Statistical Methods
In frequentist statistics, pointnull hypothesis testing based on significance tests and confidence intervals are harmonious procedures and lead to the same conclusion. This is not the case in the domain of the Bayesian framework. An inference made about the pointnull hypothesis using Bayes factor may lead to an opposite conclusion if it is based on the Bayesian credible interval. Bayesian suggestions to test pointnulls using credible intervals are misleading and should be dismissed. A null hypothesized value may be outside a credible interval but supported by Bayes factor (a Type I conflict), or contrariwise, the null value may be inside ...
Sampling The Porridge: A Comparison Of Ordered Variable Regression With F And R^{2} And Multiple Linear Regression With Corrected F And R^{2} In The Presence Of Multicollinearity, Grayson L. Baird, Stephen L. Bieber
Sampling The Porridge: A Comparison Of Ordered Variable Regression With F And R2 And Multiple Linear Regression With Corrected F And R2 In The Presence Of Multicollinearity, Grayson L. Baird, Stephen L. Bieber
Journal of Modern Applied Statistical Methods
Differences between the multiple linear regression model with Corrected R^{2} and Corrected F and the ordered variable regression model with R^{2} and F when intercorrelation is present are illustrated with simulated and realworld data.
A New Liu Type Of Estimator For The Restricted Sur Estimator, Kristofer Månsson, B. M. Golam Kibria, Ghazi Shukur
A New Liu Type Of Estimator For The Restricted Sur Estimator, Kristofer Månsson, B. M. Golam Kibria, Ghazi Shukur
Journal of Modern Applied Statistical Methods
A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived. The new estimator may have a lower MSE than the traditional estimators. It was shown using simulation techniques the new shrinkage estimator outperforms the commonly used estimators in the presence of multicollinearity.
Parametric And NonParametric Tests For The Comparison Of Two Samples Which Both Include Paired And Unpaired Observations, Ben Derrick, Paul White, Deirdre Toher
Parametric And NonParametric Tests For The Comparison Of Two Samples Which Both Include Paired And Unpaired Observations, Ben Derrick, Paul White, Deirdre Toher
Journal of Modern Applied Statistical Methods
Samples that include both independent and paired observations cause a dilemma for researchers that covers the full breadth of empirical research. Parametric approaches for the comparison of two samples using all available observations are considered, under normality and nonnormality. These approaches are compared to naive and newly proposed nonparametric alternatives.
A Study Verifying The Dimensioning Of A Multivariate Dichotomized Sample In Exploratory Factor Analysis, Rosilei S. Novak, Jair M. Marques
A Study Verifying The Dimensioning Of A Multivariate Dichotomized Sample In Exploratory Factor Analysis, Rosilei S. Novak, Jair M. Marques
Journal of Modern Applied Statistical Methods
The sample size dichotomized was related to the measure of sampling adequacy, considering the explanations provided by factors and commonalities. Monte Carlo simulation generated multivariate normal samples and varying the number of observations, the factor analysis was applied in each sample dichotomized. Results were modeled by polynomial regression based on the sample sizing.
Small Area Estimation On ZeroInflated Data Using Frequentist And Bayesian Approach, Kusman Sadik, Rahma Anisa, Euis Aqmaliyah
Small Area Estimation On ZeroInflated Data Using Frequentist And Bayesian Approach, Kusman Sadik, Rahma Anisa, Euis Aqmaliyah
Journal of Modern Applied Statistical Methods
The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zeroinflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various modelbased SAE methods for zeroinflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zeroinflation in the data. The results show that the two SAE approaches for zeroinflated data are capable to yield ...
The Importance Of Type I Error Rates When Studying Bias In Monte Carlo Studies In Statistics, Michael Harwell
The Importance Of Type I Error Rates When Studying Bias In Monte Carlo Studies In Statistics, Michael Harwell
Journal of Modern Applied Statistical Methods
Two common outcomes of Monte Carlo studies in statistics are bias and Type I error rate. Several versions of bias statistics exist but all employ arbitrary cutoffs for deciding when bias is ignorable or nonignorable. This article argues Type I error rates should be used when assessing bias.
Dynamic Conditional Correlation Garch: A Multivariate Time Series Novel Using A Bayesian Approach, Diego Nascimento, Cleber Xavier, Israel Felipe, Francisco Louzada Neto
Dynamic Conditional Correlation Garch: A Multivariate Time Series Novel Using A Bayesian Approach, Diego Nascimento, Cleber Xavier, Israel Felipe, Francisco Louzada Neto
Journal of Modern Applied Statistical Methods
The Dynamic Conditional Correlation GARCH (DCCGARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, timedependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and developing countries from different continents. The performances of indices are similar, with a joint evolution. Most index returns, especially SPX and NDX, evolve over time with a higher positive correlation.
Bivariate Analogs Of The Wilcoxon–Mann–Whitney Test And The Patel–Hoel Method For Interactions, Rand Wilcox
Bivariate Analogs Of The Wilcoxon–Mann–Whitney Test And The Patel–Hoel Method For Interactions, Rand Wilcox
Journal of Modern Applied Statistical Methods
A fundamental way of characterizing how two independent compares compare is in terms of the probability that a randomly sampled observation from the first group is less than a randomly sampled observation from the second group. The paper suggests a bivariate analog and investigates methods for computing confidence intervals. An interaction for a twobytwo design is investigated as well.
Regression When There Are Two Covariates: Some Practical Reasons For Considering Quantile Grids, Rand Wilcox
Regression When There Are Two Covariates: Some Practical Reasons For Considering Quantile Grids, Rand Wilcox
Journal of Modern Applied Statistical Methods
When dealing with the association between some random variable and two covariates, extensive experience with smoothers indicates that often a linear model poorly reflects the nature of the association. A simple approach via quantile grids that reflects the nature of the association is given. The two main goals are to illustrate this approach can make a practical difference, and to describe R functions for applying it. Included are comments on dealing with more than two covariates.
Analytical ClosedForm Solution For General Factor With Many Variables, Stan Lipovetsky, Vladimir Manewitsch
Analytical ClosedForm Solution For General Factor With Many Variables, Stan Lipovetsky, Vladimir Manewitsch
Journal of Modern Applied Statistical Methods
The factor analytic triad method of onefactor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closedform solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closedform solution is not prone to data dimensionality.